Parametric representation of objects in color space using one-class classifiers
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Journal Volume
8556 LNAI
Pages
300-314
Date Issued
2014
Author(s)
Abstract
Two new approaches to parametrization of specific (flame representative) part of a color space, labeled by an expert, are presented. The first concept is to apply D. Tax's one-class classifier as a steerable descriptor of such a complex volumetric structure. The second concept is based on approximation of the training data by a set of elliptic cylinders arranged along the principal components. Parameters of such elliptic cylinders describe the training set. The efficiency of the approaches has been proven by experimental study which let allowed us to compare the standard Gaussian Mixture Model based approach with the two proposed in the paper. © 2014 Springer International Publishing Switzerland.
SDGs
Type
conference paper